726 to 750 of 891 Retrieval-Augmented Generation Jobs in England

dependency mapping, service impact, and fault propagation. Work with AI/LLM engineers to provide high-quality features, embeddings, metadata, and contextual datasets for RAG and agentic AI systems. Define network data quality rules, correlation logic, and entity resolution methods. Create reusable analytical models for RAN, Core, IP/MPLS … assessment. Experience with graph analytics, entity relationship mapping, or knowledge graph concepts. Understanding of LLMs and how structured data can support AI agents and RAG systems. Experience with BigQuery or similar data warehouse/analytics platforms. Required Technical Skills Python. Statistics and ML basics. Network KPI modelling. RAN, Core ...

including data ingestion and modelling, backend services and APIs, AI orchestration, frontend experiences, deployment, and operations. Design and build production-grade AI features, including RAG across structured and unstructured investment data, agentic workflows, document intelligence, and AI-generated analytics. Build intuitive, information-dense interfaces for investment professionals, including analytical grids … React, advanced SQL, and complex data modelling, working with both structured and unstructured data. Proven applied AI/LLM experience, shipping production solutions using RAG, embeddings/vector search, agentic workflows, evaluation frameworks, and guardrails. Hands-on cloud and infrastructure expertise across Azure and Kubernetes (AKS), including containerisation ...

Kubernetes, and cloud-native infrastructure. Design and deploy scalable batch and real-time ML pipelines supporting customer-facing applications. Develop and operationalise LLM applications, RAG systems, and Generative AI capabilities for content understanding, summarisation, classification, and recommendation use cases. Build evaluation frameworks and observability solutions to monitor model quality, reliability … Intelligence, Statistics, Data Science, or a related STEM field. Experience applying Generative AI and LLMs to customer-facing products and designing, building, or scaling RAG systems. Experience in search, ranking, recommendations, personalisation, content intelligence, moderation, NLP, computer vision, or related AI domains. Experience with Airflow or comparable workflow orchestration platforms. ...

Kubernetes, and cloud-native infrastructure. Design and deploy scalable batch and real-time ML pipelines supporting customer-facing applications. Develop and operationalise LLM applications, RAG systems, and Generative AI capabilities for content understanding, summarisation, classification, and recommendation use cases. Build evaluation frameworks and observability solutions to monitor model quality, reliability … Intelligence, Statistics, Data Science, or a related STEM field. Experience applying Generative AI and LLMs to customer-facing products and designing, building, or scaling RAG systems. Experience in search, ranking, recommendations, personalisation, content intelligence, moderation, NLP, computer vision, or related AI domains. Experience with Airflow or comparable workflow orchestration platforms. ...

Transform customer journeys and service interactions using data, CCaaS platforms and AI to create seamless and consistent experiences Contribute to building and delivery of RAG pipelines, multi-agent orchestration and eval frameworks for enterprise-scale AI deployments Be a technical advisor Support client teams on AI architecture decisions, platform selection … regulated environments. Core experience Strong engineering background with production experience in Python and similar languages across AI systems Experience building or contributing to RAG pipelines, multi-agent systems or LLM-based products, ideally at meaningful scale Hands-on experience with at least one major cloud AI platform (AWS Bedrock ...

retained and reused. You will define the Data Architecture capabilities, standards and patterns needed to support current and emerging AI approaches, including Generative AI, RAG, agentic AI and Model Context Protocol (MCP).You will work with senior stakeholders, Data Owners, Data Stewards, Data Custodians, Data Governance, Enterprise and Business Architecture … managed and reused.Technology & Innovation: Strong knowledge and practical experience of modern and emerging Data and AI technologies and architecture patterns, including LLMs, Generative AI, RAG, semantic/vector capabilities, agentic AI, MCP, cloud data platforms and related integration technologies, with an understanding of Python or similar technologies for prototyping ...

Transform customer journeys and service interactions using data, CCaaS platforms and AI to create seamless and consistent experiences Contribute to building and delivery of RAG pipelines, multi‐agent orchestration and eval frameworks for enterprise‐scale AI deployments Be a technical advisor Support client teams on AI architecture decisions, platform selection … regulated environments. Core experience Strong engineering background with production experience in Python and similar languages across AI systems Experience building or contributing to RAG pipelines, multi‐agent systems or LLM‐based products, ideally at meaningful scale Hands‐on experience with at least one major cloud AI platform (AWS Bedrock ...

Transform customer journeys and service interactions using data, CCaaS platforms and AI to create seamless and consistent experiences Contribute to building and delivery of RAG pipelines, multi-agent orchestration and eval frameworks for enterprise-scale AI deployments Be a technical advisor Support client teams on AI architecture decisions, platform selection … regulated environments. Core experience Strong engineering background with production experience in Python and similar languages across AI systems Experience building or contributing to RAG pipelines, multi-agent systems or LLM-based products, ideally at meaningful scale Hands-on experience with at least one major cloud AI platform (AWS Bedrock ...

trends within the software industry Built or integrated AI/ML features into products and systems, including recommendation engines, NLP, computer vision, predictive analytics, RAG pipelines, and agents Successfully led modernisation and replatforming efforts to align legacy systems with short and long-term business needs Led and delivered solutions … platforms, and infrastructure that other teams build on Data Engineering: including platforms like Databricks for data processing and analytics Delivering production agentic systems, including RAG pipelines, vector search, and agentic frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and n8n DevSecOps & DevOps MLOps Infrastructure as Code Building a truly diverse, equitable ...

software code developmentLeverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standardsApply knowledge of tools within the Software … container platforms and platform-as-a-service environments, including Kubernetes and Cloud FoundryFamiliarity with Generative AI tools and patterns — including prompt engineering, retrieval-augmented generation, and vector search/embeddings — and safe, secure adoption practicesFamiliarity with financial services technology environments and enterprise-grade controls ...

including API design, automated testing, asynchronous processing, dependency management and production debugging. Practical experience with agent orchestration, structured tool calling, workflow state, retrieval-augmented generation and human-in-the-loop patterns. Experience building secure integrations with enterprise APIs, repositories, cloud services, CI/ ...

systems (Kafka or similar) are a plus. Shipped AI in a real product, not only notebooks: LLM APIs, tool/function calling, retrieval-augmented generation, evaluation, or document/vision extraction. You can talk about failure modes (hallucination, cost, latency, prompt injection, PII). ...

teams and a direct pathway to the Forward Deployed Engineer programme.Key ResponsibilitiesDesign and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observabilityBuild and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning ...

with at least one analytical or query engine (e.g. DuckDB, Trino, Spark, ClickHouse). Real experience building LLM/agent applications: retrieval (RAG), vector databases, and tool/function calling. A working understanding of data governance: cataloguing, metadata, lineage, and access control (RBAC/ABAC). An instinct … data: fusing sensitivity and licensing scope, enforced at the point of use, including for AI agents. Build agent-facing data access: retrieval (RAG), vector search, and APIs/MCP servers, with permissions applied before context reaches the model. Apply LLMs pragmatically to data work (metadata generation ...

theoretically. We are already building it. We are looking for a Director, Generative AI Experience Engineering to help define and deliver the next generation of AI-native customer experiences across commerce, content and customer interfaces for some of the world’s most ambitious brands. This role sits … edge rendering - Component systems and frontend orchestration AI Ecosystems & Orchestration - Experience across modern AI ecosystems including OpenAI, Gemini, Claude and OSS models RAG pipelines, vector databases, embeddings and chunking Tool calling, agentic systems, orchestration and memory frameworks Prompt engineering, prompt management and context engineering AI observability, evaluations, governance and guardrails ...

building scalable application programming interfaces, microservices, and distributed systemsPractical experience building enterprise AI applications using large language models, including agents, retrieval augmented generation, orchestration frameworks, and model optimizationDemonstrated ability to own services, components, or features end to end, from technical design through production deployment … from technical design through deployment, monitoring, production support, and continuous improvementBuild and enhance large language model application features using retrieval augmented generation, orchestration frameworks, evaluation methods, agentic workflows, and tool integrationContribute to technical designs, participate in design reviews, and identify risks, constraints, trade-offs ...

cost attribution by team and use case.Enablement & IntegrationDeploy vector database and retrieval infrastructure (e.g., Databricks AI Search) to support Document AI and RAG use cases.Align platform controls with Databricks and Unity Catalog governance patterns.Provide clear documentation, reusable patterns, and self-service onboarding guidance so delivery squads can deploy … really stand out from the crowdExperience with AI gateways, LLM proxies, or model-routing patterns.Exposure to AI observability tools, vector databases (Databricks AI Search), RAG pipelines, or non-deterministic system monitoring.Hands-on experience implementing policy-as-code, action logging, or AI kill switches.Direct experience with Okta, ZScaler, Entra ID, Unity ...

scalable application programming interfaces, microservices, and distributed systems Practical experience building enterprise AI applications using large language models, including agents, retrieval augmented generation, orchestration frameworks, and model optimization Demonstrated ability to own services, components, or features end to end, from technical design through production … from technical design through deployment, monitoring, production support, and continuous improvement Build and enhance large language model application features using retrieval augmented generation, orchestration frameworks, evaluation methods, agentic workflows, and tool integration Contribute to technical designs, participate in design reviews, and identify risks, constraints ...

closely with engineers to translate validated approaches into scalable, customer-facing solutions. Key Responsibilities: Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies, patent search). Define agentic workflows and reasoning strategies for multi-step IP tasks. Develop retrieval strategies, including hybrid search (semantic … Strong experience designing and running experiments, including model evaluation and iteration. Strong coding skills in Python. Experience with generative AI techniques (e.g., prompt engineering, RAG). Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases. Experience designing agentic workflows and reasoning strategies, with hands ...

team and use case. Enablement & Integration Deploy vector database and retrieval infrastructure (e.g., Databricks AI Search) to support Document AI and RAG use cases. Align platform controls with Databricks and Unity Catalog governance patterns. Provide clear documentation, reusable patterns, and self-service onboarding guidance so delivery squads … from the crowd Experience with AI gateways, LLM proxies, or model-routing patterns. Exposure to AI observability tools, vector databases (Databricks AI Search), RAG pipelines, or non-deterministic system monitoring. Hands-on experience implementing policy-as-code, action logging, or AI kill switches. Direct experience with Okta, ZScaler, Entra ...

world's most trusted patent data with sophisticated analytics, AI-powered insights, and purpose-built workflows. Protégé in PatentSight is LNIP's next-generation agentic AI assistant, purpose-built for strategic patent analysis. Protégé replaces complex filter-based workflows with natural language questions, surfacing structured, decision-ready insights … with experience in a dedicated AI/ML architecture, principal engineer, or distinguished engineer role. Proven track record delivering production LLM-based systems, including RAG pipelines, agentic/tool-use frameworks, and multi-step reasoning workflows. Strong proficiency in Python for AI service development; working knowledge of C# or equivalent ...

customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows.AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto … measurable GTM impact - you’ve shipped models or tools that moved metrics like conversion, cycle time, or retention.LLM/ML application experience - familiarity with RAG, prompt and tool design, vector search, evals and have leveraged AI for development.Excellent SQL skills and fluency in Python or R, with experience applying analytical ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementation Analyse complex constraints across product, data, security, legal and operations, translating them into … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

enable more reliable and scalable product development Establish end-to-end technical architectures for LLM-enabled systems, covering data pipelines, retrieval/RAG, prompt orchestration and agent workflows to support robust and effective AI implementationAnalyse complex constraints across product, data, security, legal and operations, translating them into clear … services and the supporting platform services required for production delivery AI engineering experience delivering LLM-enabled systems using LLM APIs/Bedrock, including RAG architectures, vector databases and orchestration frameworks DevOps skills including CI/CD pipelines, containerisation and monitoring/observability, with experience defining release and operating practices ...

release increment, including data flows, integration sequencing, and non-functional requirements (performance, scalability, security) baked in at design time Own the technical implementation pattern (RAG pipeline design, prompt/agent orchestration, model integration via APIs) and work directly with data science/ML engineering teams during build. Sit in sprint … program/product roadmap into solution designs and detailed architecture specs for Commercial Digital Solutions Define architecture patterns for GenAI/LLM-based solutions (RAG, agentic workflows, model orchestration), evaluate build-vs-buy for AI capabilities Architect integration patterns between Commercial systems and the enterprise data platform/data lake ...